Elucidation of validity of emotion models based on EEG feature analysis

Abstract

There have been many attempts to recognize emotions based on machine learning and biometric data, such as EEG. In these attempts used emotion model has a significant impact on the estimation of the emotions. In this study, we examined the validity of the models by comparing the pleasantness estimation based on EEG and the internal emotion estimated by the three representative psychological emotion models; Russell’s emotional dimension model, Plutchik’s Emotion Wheel, and Ekman’s basic emotions. The results showed that the pleasantness estimated by Plutchik’s model was significantly different from the pleasantness estimated by other models and EEG, and that the pleasantness estimated by Ekman’s model was closest to the pleasantness estimated by EEG. The results suggest that the model including no complex emotions such as Ekman’s model most closely matches to the objective index of brain activity in the viewpoint of the degree of pleasantness and displeasure.